Article ID: | iaor20011123 |
Country: | United Kingdom |
Volume: | 27 |
Issue: | 11/12 |
Start Page Number: | 1077 |
End Page Number: | 1092 |
Publication Date: | Sep 2000 |
Journal: | Computers and Operations Research |
Authors: | Balakrishnan Nagraj, John Caron H. St., Fiet James O. |
Keywords: | neural networks, statistics: multivariate |
In this paper, we hypothesize that there is a non-linear relationship between corporate strategy, short-run financial variables, and wealth creation measured as market value added, and use neural networking to model this relationship. The neural network model accurately categorized over 90% in the training set and nearly 93% of firms in the holdout test sample. Additional analysis revealed that strategy variables were particularly effective predictors of an upward trend in wealth creation whereas short-run financial variables were more effective in predicting a downward trend, or wealth destruction. Neural networks outperformed discriminant analysis in predictive ability in all analyses, suggesting the presence of non-linear effects. This research represents a first attempt to use neural networking to model the relationship between corporate strategy and wealth creation.